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Randomness in Evolution: Valid and Invalid Usage

Yeah, this was something that also confused me about zosima's post. I interpreted the property of being "closed under the integers" as being the property of algebraic closure you described above. As you also noted this propert say nothing about the discreteness or continuity of the dynmical system.

this may be partially my fault; in 982 i said the simualtions of the logistic map on a digital computer was a many to one map on the integers. to the extent that a digital computer is a finite state machine, this is trivially true.

it means digital computers cannot simulate chaotic processes in the long run (as all trajectories eventually fall onto digitally-periodic orbits of finite length).

analogue computers, on the other hand, are not finite state machines; but then we cannot program the equations precisely and so a simulation on an analogue computer to the chaotic equations we were hoping to investigate.
 
Your claim that the behavior of the computer itself is not 'truly chaotic' is demonstrably false. See: http://mathworld.wolfram.com/ShadowingTheorem.html
QED: Computers can be chaotic.

read the theorem more carefully. it does not establish that computer simulations are chaotic.

(it says more about the flexibility of chaotic systems than digital simulations!)

how could the trajectory of a finite state machine not approach a periodic cycle?
 
If you made two digital computer simulations of chaotic system, they will not dverge, if you set the initial numbers and precision to be the same.
agreed.
If you made two analogue computer simulations of chaotic system, they will diverge, by virtue of them not having identical starting conditions.

agreed.

but then the physical componets will not be exactly the same either so the two analogue computers will be simulating different systems.
(and would be expected to diverge even if one could use the same initial condition, no?)

simulations on analogue machines imply structural error so the equations realised (if you believe that corresponding equations exist) will differ from the mathematical target system as well.

this is one reason so few systems have been proven to be chaotic, it took three decades to prove the Lorenz 1963 equations "really" were chaotic.
the proof for the logistic map at "r=4" does not generalise (one turns it into the doubling map by a change of variable which works only for r=4).
 
read the theorem more carefully. it does not establish that computer simulations are chaotic.
What the shadowing theorem proves is that the computational path has a true analog in the chaotic system, that is the path of the shadow. But I'll agree that It is more accurate to say it proves effective chaos, or as chaotic as is possible in a physical system with finite resources. I don't think any physical system, analog or digital can be truly chaotic in the precise mathematical terminology and I've stated that several different times.

When we're talking about physical systems, we're really talking about effective definitions and approximations.

how could the trajectory of a finite state machine not approach a periodic cycle?
In finite memory a system will definitely approach a finite periodic cycle. I would argue that that it is effectively chaotic up until the cycle begins to repeat. The length of which would be limited by the size of available computational resources. In infinite time&memory( ie an abstract computational machine), you could have a period of infinite length.

In any physical system(analog or digital) there will be finite resources and thus a finite set of possible states(although it might be harder to visualize what these states are in an analog system). Once the system runs out of distinct states, the next step must land in a repeating state by the pigeon hole principle.

I agree that an analog system must be controllable to be a simulation of anything. But I don't think it is the case that no analog system is controllable. Only that the system has finite limitations. After which it will diverge from the theoretical. Often analog systems are limited by the available energy or system noise. It can often be difficult to create a controllable chaotic simulation in an analog system because noise will create stability in the system. So beyond a certain number of iterations the error in the analog system converges.

ETA: An example of one technique for making an analog system controllable is to use a low pass filter. Then your system is an accurate approximation of the mathematical ideal up to the efficiency of the filter.
 
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but then the physical componets will not be exactly the same either so the two analogue computers will be simulating different systems.
(and would be expected to diverge even if one could use the same initial condition, no?)

simulations on analogue machines imply structural error so the equations realised (if you believe that corresponding equations exist) will differ from the mathematical target system as well.

Yes, which is why I made the following statement, with highlighting.
An analogue system aims to be a physical analogue of the system that is modelled, so there is no numerical approximation. In other words, a chaotic analogue simulation will be chaotic, but obviously its behaviour will diverge from that of the system it is modelling because it is a different chaotic system.
 
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Yes, which is why I made the following statement, with highlighting.
An analogue system aims to be a physical analogue of the system that is modelled, so there is no numerical approximation. In other words, a chaotic analogue simulation will be chaotic, but obviously its behaviour will diverge from that of the system it is modelling because it is a different chaotic system.

So you're claiming that to make an 'analog model' of the weather on earth we would have to make a duplicate of earth and copy it down to the highest level of detail, like Slartibartfast in Hitchikers Guide to the Galaxy? But since we couldn't copy the earth perfectly the second time(for example we might use different fjords) it would have a different time evolution?

What system aims to be the physical instantiation of e^x?

Treatment of an analog model like there is some sort of one-to-one correspondence leads to a lot more problems than just initial conditions. Approaching it from the perspective of abstracting a controllable system is much more sensible.
 
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so i think we are close to agreement, but when you say
In other words, a chaotic analogue simulation will be chaotic, but obviously its behaviour will diverge from that of the system it is modelling because it is a different chaotic system.

i would claim all you have evidence for is that the physical analoge is a "different system" not a "different chaotic system." given that it is different from the target mathematical system, we have no evidence that the physical analogue is itself chaotic, only that is it different.

so we can prove digital simulations are NOT chaotic, but we cannot prove analogue simulations are.

do we agree on this?

in fact, i know of no examples where the target equations turn out to be the best model of the output of an analogue computer simualtion, given a large set of obs from the simulation. i find it quite rational to be skeptical that any set of "governing" equations exist. which is a weak link back into evolution...
 
Outside of some bounded interval ([0,1] in the case of the logistic, any intial interval will eventually not map into itself.

Nice assertions. Would you care to back them up with some actual examples?
I already showed these claims to be false.

But when r is restricted to the integers, the dynamical system is not chaotic, as there are no periodic orbits and periodic orbits are an essential element of chaos.

But just for the sake of thoroughness, I thought I'd hammer this one home.
Since Mijo seems to value arguments from authority more than ones via reasoning...

http://mathworld.wolfram.com/Chaos.html

They do state the constraints Mijo mentioned as general guidelines. (But neither necessary nor sufficient conditions.)

More importantly note:
Wolfram's Mathworld said:
"However, it should be noted that despite its "random" appearance, chaos is a deterministic evolution. In addition, there are chaotic systems that do not have periodic orbits (periodic orbits only survive in the boundaries of KAM tori, and for sufficiently strong perturbations from the integrable case, islands do not necessarily survive). Furthermore, in so-called quantum chaos, trajectories do not diverge exponentially because they are constrained by the fact that the entire evolution must be unitary."

We conclude from this:
1. Chaotic systems are deterministic.
2. There are chaotic systems that do not have periodic orbits.

Also:
Wolfram's Mathworld said:
"Chaos" is a tricky thing to define. In fact, it is much easier to list properties that a system described as "chaotic" has rather than to give a precise definition of chaos.

Gleick (1989, p. 306) notes that "No one [of the chaos scientists he interviewed] could quite agree on [a definition of] the word itself," and so instead gives descriptions from a number of practitioners in the field. For example, he quotes Philip Holmes (apparently defining "chaotic") as, "The complicated aperiodic attracting orbits of certain, usually low-dimensional dynamical systems." Similarly, he quotes Bai-Lin Hao describing chaos (roughly) as "a kind of order without periodicity."

It turns out that even textbooks devoted to chaos do not really define the term. For example, Wiggins (1990, p. 437) says, "A dynamical system displaying sensitive dependence on initial conditions on a closed invariant set (which consists of more than one orbit) will be called chaotic." Tabor (1989, p. 34) says, "By a chaotic solution to a deterministic equation we mean a solution whose outcome is very sensitive to initial conditions (i.e., small changes in initial conditions lead to great differences in outcome) and whose evolution through phase space appears to be quite random." Finally, Rasband (1990, p. 1) says, "The very use of the word 'chaos' implies some observation of a system, perhaps through measurement, and that these observations or measurements vary unpredictably. We often say observations are chaotic when there is no discernible regularity or order."

This puts us in the position of defining chaos. In that context I return to my contention that in the context of evolution a definition of exponential error growth is the most appropriate definition for chaos in this discussion. Insofar as it is possible that there could be unbounded systems without periodic orbits that are models of evolution. If they are behaving in a way that is unpredictable with respect to initial conditions, it seems logical to include them under the aegis of chaos. This is doubly true in the status quo where no reasoning has been proposed for why periodic orbits in a bounded region are necessary and/or relevant.
 
so i think we are close to agreement, but when you say
In other words, a chaotic analogue simulation will be chaotic, but obviously its behaviour will diverge from that of the system it is modelling because it is a different chaotic system.

i would claim all you have evidence for is that the physical analoge is a "different system" not a "different chaotic system." given that it is different from the target mathematical system, we have no evidence that the physical analogue is itself chaotic, only that is it different.

so we can prove digital simulations are NOT chaotic, but we cannot prove analogue simulations are.

do we agree on this?

in fact, i know of no examples where the target equations turn out to be the best model of the output of an analogue computer simualtion, given a large set of obs from the simulation. i find it quite rational to be skeptical that any set of "governing" equations exist. which is a weak link back into evolution...

Maybe I have a slightly different interpretation to you. I would say that you could theoretically produce an anlalogue "computer" that exhibits true chaotic behaviour; one could attempt to model it (and assess whether the idealised components) would produce a chaotic system, Chua's circuit for example, could be used as a model for some other physical system.

Of course, the best that you could produce is a different chaotic system so it would be impractical, and except over short timescales, all it would model well would be itself...

Back to your last point.

i find it quite rational to be skeptical that any set of "governing" equations exist. which is a weak link back into evolution...

Agreed. On reflection, I do not think that evolution is a chaotic system, but that it is influenced by many systems that are, and that there are many nonlinear feedback loops which mean that it is equally as affected by small chance events being magnified into significant events, including events due to "disruptive mutations" that skew the whole ecosystem, or fitnes landscape. When these will occur is random, and which occurs first will affect what can come afterwards.

It is probably easiest to talk about viral mutations altering the virulance of diseases. A bird-flu epedemic that affects the number of insect-eating birds could easily affect vegitation cover in a large area, and albedo and transpiration rate, on a scale the is definitely sufficient to affect weather.

Similarly if a pandemic alters human economic activity, this would directly affect the weather. This effect has been shown in the aftermath of September 11th
The grounding of planes for three days in the United States after September 11, 2001 provided a rare opportunity for scientists to study the effects of contrails on climate forcing. Measurements showed that without contrails, the local diurnal temperature range (difference of day and night temperatures) was about 1 degree Celsius higher than immediately before;[4] however, it has also been suggested that this was due to unusually clear weather during the period.[5]
 
We conclude from this:
1. Chaotic systems are deterministic.
2. There are chaotic systems that do not have periodic orbits

By definition, chaotic systems are deterministic. no argument there.

But i know of no chaotic system that does not have a pretty interesting set of (unstable) periodic orbits. can you give us an example (your quote does not suggestion there are no periodic orbits, only that there are no islands.)

i would be very interested to learn of an example!
 
and except over short timescales, all it would model well would be itself...

agreed.

and defining chaos requires taking limits in the long time limit. (if Lyapunov exponents are to play a role in defining chaos, and they usually are.)

so without access to the "underlying" equations which "govern" the physical system, which we never have access too, we cannot prove a physical system is chaotic. or periodic for that matter!

ed lorenz used to say that it was reasonable to call a system chaotic if our best model of that system was chaotic, while never forgetting that tomorrow's best model might be rather different from today's best model.

i expect that this is about the best we can do.
 
A bird-flu epedemic that affects the number of insect-eating birds could easily affect vegitation cover in a large area, and albedo and transpiration rate, on a scale the is definitely sufficient to affect weather.

i know of quotes illustrating that the atmosphere was sensitive to initial condition which date back to Poe.

this one from Sir Arthur Eddington (1928)

A total eclipse of the sun, visible in Cornwall is prophesied for 11
August 1999... I might venture to predict that 2+2 will be equal to 4 even
in 1999... The prediction of the weather this time next year ... is not
likely to ever become practicable... We should require extremely detailed
knowledge of present conditions, since a small local deviation can exert
an ever-expanding influence. We must examine the state of the sun ... be
forewarned of volcanic eruptions, ..., a coal strike ..., a lighted match
idly thrown away...

and i expect your bird-flu epedemic would qualify to join the list.
 
By definition, chaotic systems are deterministic. no argument there.

But i know of no chaotic system that does not have a pretty interesting set of (unstable) periodic orbits. can you give us an example (your quote does not suggestion there are no periodic orbits, only that there are no islands.)

i would be very interested to learn of an example!

Well I think in they use the Kam Tori, because it is considered chaotic even in the regions of parameter space where it is not behaving periodically.

Personally, one of my favorite examples is 'Rule 30'. It has a growth that is approximately: f(x) ~= 4*f(x-1), If its output is plotted with respect to magnitude, it will actually just look like e^x with small deviations. Yet if you look at the patterns occurring in the digits, it has a lot of chaos buried in its output. Its one of the simplest and yet complex and amazing systems that I am aware of. It is actually used as the random number generator in Mathematica(as opposed to the traditional linear congruential generators). 'Rule 110' is even more amazing. It is actually Turing complete. So if you find cellular automata interesting, check 110 out too.

http://mathworld.wolfram.com/Rule30.html

These systems are both discrete time and discrete state systems, nor do they have periodic orbits(well since 110 is universal, technically it can have periodic orbits, although it is chaotic in regions of parameter space that are non-periodic).

Actually, since so much of the work done in chaos theory is experimental rather than deductive, I think that systems with periodic orbits are often studied more frequently out of expediency. In a bounded system the state representation is growing towards more and more insignificant digits rather than more and more significant ones. If you are truncating insignificant digits the shadow theorem tells us that you will not fundamentally change the nature of equation you were modeling. If you start truncating* the significant digits of the state vector, you no longer have such a guarantee. Thus it can be far trickier to simulate a system that is growing exponentially, than one that is more stationary in its most significant digits.

One thing that I think** will always be true of a chaotic system is that it will always have a representation that grows exponentially. This is a necessary precondition for exponential error growth from nearby positions. I think there are probably just as many chaotic systems that grow 'up' as grow 'down'.(Although I can't prove it in any way)

*Truncating the state vector being distinct from truncation only for the purpose of plotting the output.
**Opinion not put forth as fact.
 
i would be very interested to learn of an example!
Well I think in they use the Kam Tori, because it is considered chaotic even in the regions of parameter space where it is not behaving periodically.

thanks.

for a given dynamical system, some trajectories will be chaotic, others not. initial conditions on a torus are "quasi-periodic" (for a common two dimensional torus, that would correspond to something like the sum of two sinusiods with incommensurate periods, so the trajectory never quite repeats itself). along with the tori are real periodic orbits. proofs here go back to poincare et al.

as you change a parameter in the system, the tori start to "break up", but you never get rid of the periodic orbits; i work more with disipative systems than with the hamiltonian systems that KAM applies too, but i am pretty sure the periodic orbits never go away.

so i don't think this qualifies as example, but remain interested!
 
so i don't think this qualifies as example, but remain interested!
I guess you'll have to take it up with Mathworld,
i'd be happy too. but while i see that they claim the tori are lost, i find no claim that the chaotic sea is not littered with unstable periodic orbits. so as far as i can tell mathworld makes no claim that this is an example. i remain happy to follow up pointers to specific text
did you read about rule 30?
yep. it is a nice CA.
 
i'd be happy too. but while i see that they claim the tori are lost, i find no claim that the chaotic sea is not littered with unstable periodic orbits. so as far as i can tell mathworld makes no claim that this is an example. i remain happy to follow up pointers to specific text

Is there a standard definition of "island" in dynamical sytems theory or chaos theory?

Does an island have to be a continous interval where all the orbits are periodic? Can it be as subinterval where only isolated, indivdual periodic orbits exits but are "closer" to one another than they are to any other orbits in a larger subinterval?

yep. it is a nice CA.

I think that zosima's point was that the cell's color as a function of its position for each iteration of the CA is aperiodic for every cell in the array. Apparently, there is a proof that no two columns of rule 30 can ever repeat. Unfortunately, it is not publicly availble (as is the case with the proof that rule 110 is universal.

Nonetheless, there does seem to some order in the left-most cells of rule 30 (e.g., repeating left-shifted "L" shapes in the "first" columns of the automaton, but, as this behavior is not described by the defintion of "periodicity" that I am most familiar with I am not sure how relevant it is to the discussion.
 
Is there a standard definition of "island" in dynamical sytems theory
yep. they correspond to a certain type of trajectory in the state space. take a look at the text and figures here for the "standard map". islands correspond to quasi-periodic orbits (two or more incommensurate frequencies) in hamiltonian maps or flows. as a parameter (in this case "k") is increased the islands break up into "cantori" and no longer present barriers to trajectories. of course in dimensions greater than 3, a 2-D torus is not a barrier anyway... but from figures 1 & 2 you can see why they are called islands.
Does an island have to be a continous interval where all the orbits are periodic?
you require a 2-d (or a 3-d flow) or higher dimensional dynamical system.

motion on a circle (in 2-d) or the surface of a torus (in 3-d) is quasi-periodic, not periodic. even after the tori break up, unstable periodic orbits remain.
 
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Back to the OP (you can see why I started a different thread about chaotic systems...)

It is indisputible that there are many highly nonlinear feedback loops in ecosystems. Because of these interactions between organsims, sometimes smalll chance changes in one organism can have a large effect on the whole ecosystem, and thus on the "fitness landscape" and course of evolution for many organisms.

These chance effects are likely to be more important when the fitness landscape is more plastic, which will be when the environment is changing, or has changed recently*. This assertion is borne out by the fossil evidence, where most major "explosions of diversification" occur after mass extinctions, where the environment has changed, and there are many empty niches available.

I'll reiterate the following post (hidden for brevity) because this is the sort of situation that I would expect to occur (infrequently), whereas denying the probabilistic nature seems to make it hard to explain why only one colony in many evolved the ability to metabolise citrate.

With evolution, chance events become more important over time, they don't average out as in "well behaved systems".

Why isn't this an example of a random evolutionary event:

From New Scientist
Mostly, the patterns Lenski saw were similar in each separate population. All 12 evolved larger cells, for example, as well as faster growth rates on the glucose they were fed, and lower peak population densities.

But sometime around the 31,500th generation, something dramatic happened in just one of the populations – the bacteria suddenly acquired the ability to metabolise citrate, a second nutrient in their culture medium that E. coli normally cannot use.

Indeed, the inability to use citrate is one of the traits by which bacteriologists distinguish E. coli from other species. The citrate-using mutants increased in population size and diversity.



In the meantime, the experiment stands as proof that evolution does not always lead to the best possible outcome. Instead, a chance event can sometimes open evolutionary doors for one population that remain forever closed to other populations with different histories.

That argument that a chance event can open (or close) evoloutionary "doors") has been part of my argument, although I talked about "niches".


I notice that articulett is claiming to have put me on ignore again:

And jimbob... I've explained my point a thousand times. The nuts get to the top through probabilities, I supposed... but that IS irrelevant to understanding how they always seem to end up there. And I have you on ignore. Don't bother asking me leading questions you cannot comprehend the answer to anyhow. That is mijo-esque. I've been there; done that. You can have the last word. I refuse to let you inflict it on me, however.

(Your obfuscation regarding probabilities is fantastic, however, if you don't really want people to understand the basic science that ensures that the big nuts will settle on top... if, instead, you hope that they'll be open to the idea that there is a plot amongst nut sellers to make it look like there are more big nuts then there actually are. kudos.)


I say that Dawkins in the Extended Phenotype uses a probabilistic treatment of natural selection.

What is the alternative "nonprobabilistic" treatment?

That is obviously somehow a dishonest question.



*Because initially, the organisms will not be so optimised to the new environment as before. When near an optimum, random changes would be more likely to move away from the optimum (these are selected against so don't move the organism away from the optimum), whilst if further away, more changes will be likely to move nearer an optimum, maybe a different optimum. Again this is borne out by fossil evidence, where the evolution rate (as measured by acquisition of "modern features") slows over time in new types of molluscs {from memory from Maynard Smith's "Theory of Evolution"}
 
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I think that zosima's point was that the cell's color as a function of its position for each iteration of the CA is aperiodic for every cell in the array. Apparently, there is a proof that no two columns of rule 30 can ever repeat. Unfortunately, it is not publicly availble (as is the case with the proof that rule 110 is universal.

It is relevant by showing a system that does not show periodic orbits yet is chaotic. It demonstrates that the definition of chaos that you have provided is too restrictive.

Although this spat over the definition of chaos is irrelevant insofar as we're talking about evolution. Whether we call them apples or oranges doesn't matter.
 

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